Survey on web spam detection

Spirin, N. А.; Han, Jiawei

doi:10.1145/2207243.2207252

Cited by 209 publications

(21 citation statements)

References 73 publications

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“…In this, the spammers create a link indirectly which innocent people does not know the impact behind that links which that makes spammers to get easily accessed their accounts or to advertise their products. (Wang and Lin, 2011;Abu-Nimeh and Chen, 2010) General web spam (Spirin and Han, 2012) The main concept behind this Sblogs is to link other websites.…”

Section: Indirect Spammentioning

confidence: 99%

Detecting spams in social networks using ML algorithms - a review

Murugan

Devi

2018

IJEWM

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The social network, by the name which has popularised in today's world and growing rapidly at all times and controlling over mankind. The social networks like Twitter, Facebook, and LinkedIn, etc., have become a regular and daily usage of many people. It becomes a good mediator for the people who would like to share some posts, are some of their own videos, or some messages. But there has been major issues that the particular user of the social networks like Twitter and Facebook have the problem of indiscipline actions which we call as spam, by the third person who is knowingly doing this to spoil their intention and good opinion upon each other. Also, these spams help to steal information about the people who using social networks. In this paper, we study and analyse about the spam in social networks and machine learning algorithms to detect such kind of spams. This paper also focuses on the ML algorithms detection rate and false positive rate over different datasets.

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Section: Indirect Spammentioning

confidence: 99%

Detecting spams in social networks using ML algorithms - a review

Murugan

Devi

2018

IJEWM

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“…Beberapa diantaranya sering ditemukan komentar, trackback, dan pingback spam pada tulisan (blog) yang diposting seseorang [7]. Menurut Hines pada [7] [9] deteksi spam kebanyakan menggunakan metode berbasis isi teks / tulisan yang ditulis. Hal ini dapat dilakukan dengan menggunakn beberapa metode seperti algoritma klasifikasi pada teks seperti algoritma Naive Bayes [10] dan Support Vector Machine [11].…”

Section: A Tinjauan Pustakaunclassified

Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes

Lukito

2017

Ultimatics

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Instagram is the most famous pictures and videos media sharing based on the web & mobile application. Instagram users can have picture posts that can be commented by their followers. Indonesian public figures such as actors, actresses, musicians use Instagram to promote their activities to their followers. Unfortunately, there are a lot of spam comments in Instagram that need special attention and have to be removed. This research grabs Instagram comments and builds the dataset from Indonesian public figures who have more than one million followers. By using preprocessing (tokenization, stop words removal, and stemming), TF-IDF weighting, and supervised learning, Naive Bayes method is used to detect spam comments in Indonesian. Naive Bayes produces 74,31% accuracy rate on unbalanced datasets and 77,25% accuracy rate on balanced datasets. This result shows that Naïve Bayes can be used to build an automatic Indonesian spam comments detector on Instagram with high accuracy rate. The novelty of this research is that Naive Bayes can be used to detect spam comment on our Indonesian Instagram comments dataset. Index Terms—Instagram, Naive Bayes, Indonesian spam comments, spam comments detection.

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“…2) which are currently challenge for search engines i.e. Content Spam, Link Spam, Cloaking & Redirection and Click Spam [21].…”

Section: A Taxonomy Of Web Spammentioning

confidence: 99%

Study on the Effectiveness of Spam Detection Technologies

Iqbal¹,

Abid²,

Ahmad³

et al. 2016

IJITCS

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Abstract-Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.

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Survey on web spam detection

Cited by 209 publications

References 73 publications

Detecting spams in social networks using ML algorithms - a review

Detecting spams in social networks using ML algorithms - a review

Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes

Study on the Effectiveness of Spam Detection Technologies

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